


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor

SCREAMING_SNAKE_CASE						:int			=  random.Random()



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Optional[int]=1.0		,    lowerCAmelCase_				:str=None		,    lowerCAmelCase_				:int=None				)->Tuple:
   '''simple docstring'''






   if rng is None:
      snake_case_            =       global_rng

   snake_case_            =       []
   for batch_idx in range(shape[0]				):
      values.append([]				)
      for _ in range(shape[1]				):
         values[-1].append(rng.random() * scale				)

   return values


@require_torch
@require_torchaudio
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]=7     ,					_lowerCAmelCase							:							Tuple=4_0_0     ,					_lowerCAmelCase							:							Union[str, Any]=2_0_0_0     ,					_lowerCAmelCase							:							List[str]=2_4     ,					_lowerCAmelCase							:							List[Any]=2_4     ,					_lowerCAmelCase							:							Union[str, Any]=0.0     ,					_lowerCAmelCase							:							Tuple=1_6_0_0_0     ,					_lowerCAmelCase							:							Optional[Any]=True     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       min_seq_length
     snake_case_            =       max_seq_length
     snake_case_            =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     snake_case_            =       feature_size
     snake_case_            =       num_mel_bins
     snake_case_            =       padding_value
     snake_case_            =       sampling_rate
     snake_case_            =       return_attention_mask
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """simple docstring"""
     return {
         "feature_size": self.feature_size,
         "num_mel_bins": self.num_mel_bins,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any=False     ,					_lowerCAmelCase							:							Optional[Any]=False	) ->			List[str]:




     """simple docstring"""






     def _flatten(_lowerCAmelCase							:							List[str]	):
        return list(itertools.chain(*_lowerCAmelCase	)	)

     if equal_length:
        snake_case_            =       [floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
     else:
        # make sure that inputs increase in size
        snake_case_            =       [
            floats_list((x, self.feature_size)	)
            for x in range(self.min_seq_length     ,					self.max_seq_length     ,					self.seq_length_diff	)
        ]
     if numpify:
        snake_case_            =       [np.asarray(_lowerCAmelCase	) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class      __lowerCAmelCase  (			__snake_case			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							SpeechaTextFeatureExtractor if is_speech_available() else None



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       SpeechaTextFeatureExtractionTester(self	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			Optional[Any]:




     """simple docstring"""
     self.assertTrue(np.all(np.mean(_lowerCAmelCase     ,					axis=0	) < 1e-3	)	)
     self.assertTrue(np.all(np.abs(np.var(_lowerCAmelCase     ,					axis=0	) - 1	) < 1e-3	)	)



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     # create three inputs of length 800, 1000, and 1200
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs]

     # Test feature size
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					padding=_lowerCAmelCase     ,					return_tensors="np"	).input_features
     self.assertTrue(input_features.ndim == 3	)
     self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

     # Test not batched input
     snake_case_            =       feature_extractor(speech_inputs[0]     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(np_speech_inputs[0]     ,					return_tensors="np"	).input_features
     self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)

     # Test batched
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)

     # Test 2-D numpy arrays are batched.
     snake_case_            =       [floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     snake_case_            =       np.asarray(_lowerCAmelCase	)
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)



  def        lowerCAmelCase__	(  self							:							int	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]

     snake_case_            =       ["longest", "max_length", "do_not_pad"]
     snake_case_            =       [None, 1_6, None]
     for max_length, padding in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       feature_extractor(
            _lowerCAmelCase     ,					padding=_lowerCAmelCase     ,					max_length=_lowerCAmelCase     ,					return_attention_mask=_lowerCAmelCase	)
        snake_case_            =       inputs.input_features
        snake_case_            =       inputs.attention_mask
        snake_case_            =       [np.sum(_lowerCAmelCase	) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]

     snake_case_            =       ["longest", "max_length", "do_not_pad"]
     snake_case_            =       [None, 1_6, None]
     for max_length, padding in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       feature_extractor(
            _lowerCAmelCase     ,					max_length=_lowerCAmelCase     ,					padding=_lowerCAmelCase     ,					return_tensors="np"     ,					return_attention_mask=_lowerCAmelCase	)
        snake_case_            =       inputs.input_features
        snake_case_            =       inputs.attention_mask
        snake_case_            =       [np.sum(_lowerCAmelCase	) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6	)
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6	)
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			int:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]
     snake_case_            =       feature_extractor(
         _lowerCAmelCase     ,					padding="max_length"     ,					max_length=4     ,					truncation=_lowerCAmelCase     ,					return_tensors="np"     ,					return_attention_mask=_lowerCAmelCase     ,					)
     snake_case_            =       inputs.input_features
     snake_case_            =       inputs.attention_mask
     snake_case_            =       np.sum(attention_mask == 1     ,					axis=1	)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
     self._check_zero_mean_unit_variance(input_features[1]	)
     self._check_zero_mean_unit_variance(input_features[2]	)



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]
     snake_case_            =       feature_extractor(
         _lowerCAmelCase     ,					padding="longest"     ,					max_length=4     ,					truncation=_lowerCAmelCase     ,					return_tensors="np"     ,					return_attention_mask=_lowerCAmelCase     ,					)
     snake_case_            =       inputs.input_features
     snake_case_            =       inputs.attention_mask
     snake_case_            =       np.sum(attention_mask == 1     ,					axis=1	)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
     self._check_zero_mean_unit_variance(input_features[2]	)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape     ,					(3, 4, 2_4)	)

     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]
     snake_case_            =       feature_extractor(
         _lowerCAmelCase     ,					padding="longest"     ,					max_length=1_6     ,					truncation=_lowerCAmelCase     ,					return_tensors="np"     ,					return_attention_mask=_lowerCAmelCase     ,					)
     snake_case_            =       inputs.input_features
     snake_case_            =       inputs.attention_mask
     snake_case_            =       np.sum(attention_mask == 1     ,					axis=1	)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
     self._check_zero_mean_unit_variance(input_features[2]	)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape     ,					(3, 6, 2_4)	)



  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     import torch

     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       np.random.rand(1_0_0     ,					3_2	).astype(np.floataa	)
     snake_case_            =       np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
        snake_case_            =       feature_extractor.pad([{"input_features": inputs}]     ,					return_tensors="np"	)
        self.assertTrue(np_processed.input_features.dtype == np.floataa	)
        snake_case_            =       feature_extractor.pad([{"input_features": inputs}]     ,					return_tensors="pt"	)
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							str	) ->			List[Any]:




     """simple docstring"""
     from datasets import load_dataset

     snake_case_            =       load_dataset("hf-internal-testing/librispeech_asr_dummy"     ,					"clean"     ,					split="validation"	)
     # automatic decoding with librispeech
     snake_case_            =       ds.sort("id"	).select(range(_lowerCAmelCase	)	)[:num_samples]["audio"]

     return [x["array"] for x in speech_samples]



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       np.array([
         -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
         -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
         -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
     ]	)
     # fmt: on

     snake_case_            =       self._load_datasamples(1	)
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="pt"	).input_features
     self.assertEquals(input_features.shape     ,					(1, 5_8_4, 2_4)	)
     self.assertTrue(np.allclose(input_features[0, 0, :3_0]     ,					_lowerCAmelCase     ,					atol=1e-4	)	)








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							=							{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							['MobileBertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileBertForMaskedLM',
		    'MobileBertForMultipleChoice',
		    'MobileBertForNextSentencePrediction',
		    'MobileBertForPreTraining',
		    'MobileBertForQuestionAnswering',
		    'MobileBertForSequenceClassification',
		    'MobileBertForTokenClassification',
		    'MobileBertLayer',
		    'MobileBertModel',
		    'MobileBertPreTrainedModel',
		    'load_tf_weights_in_mobilebert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileBertForMaskedLM',
		    'TFMobileBertForMultipleChoice',
		    'TFMobileBertForNextSentencePrediction',
		    'TFMobileBertForPreTraining',
		    'TFMobileBertForQuestionAnswering',
		    'TFMobileBertForSequenceClassification',
		    'TFMobileBertForTokenClassification',
		    'TFMobileBertMainLayer',
		    'TFMobileBertModel',
		    'TFMobileBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mobilebert import (
		    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileBertConfig,
		    MobileBertOnnxConfig,
		)
		from .tokenization_mobilebert import MobileBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mobilebert_fast import MobileBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilebert import (
				    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertLayer,
				    MobileBertModel,
				    MobileBertPreTrainedModel,
				    load_tf_weights_in_mobilebert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilebert import (
				    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertMainLayer,
				    TFMobileBertModel,
				    TFMobileBertPreTrainedModel,
				)

else:
		import sys

		lowercase_							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)